This report presents the results of the simulations from an integrated model of consumer food waste. This is the first step in developing a model that can assess the impact of policy interventions on reducing food waste among consumers. Final aim is to create a predictive and dynamic policy support tool for a road map for the 50% reduction of European food waste by 2030. This model combines an Agent Based Model and a Bayesian Network.

In developed countries an estimated 30 to 40% of food is wasted; about half of this waste stems from consumers, while the remainder is lost through farm practices, transport and processing, as well as in a retail setting. To meet target 12.3 of the Sustainable Development Goals of “halving per capita food waste and reducing food losses by 2030”, there needs to be a better understanding of the drivers of consumers’ food waste (as the largest single contributor to food waste). More importantly, the effectiveness of interventions designed to reduce food waste at consumer level needs to be assessed.

Using a simulation approach is necessary for assessing food waste, because empirical data are very limited. Where data are available, they have a high potential for bias (such as self-reported consumer food waste), or are limited in scale. This leads to high levels of uncertainty in the available data, additional to the complexity associated with understanding the socio-economic drivers of food waste. Bayesian Networks (BNs) can incorporate uncertainty and complexity in the model structure, but are less effective at incorporating behavioural factors (i.e. idiosyncratic biases of single actors, and interactions among actors) and temporal dynamics (interaction among variables or actors across time). For these types of information, Agent-Based Models (ABMs) are much better suited. To better represent food system complexity whilst incorporating the interactions among and within actors (businesses, consumers, etc.), there is a need for BNs and ABMs to interact dynamically.

Here, the first test of a fully-integrated model consisting of an ABM and of a BN is outlined, along with initial results from simulation runs. The integrated model uses data on consumers from the REFRESH pilot countries.

 

Citation: 

Grainger, M., Piras, S., Righi, S., Setti, M., Stewart, G., Vittuari, M.. 2018. Behavioural economics: Linking Bayesian and agent-based models to assess consumer food waste. REFRESH Deliverable D4.4

Language: 

  • English

Publishing date: 

11/06/2019

Language: 

Citation: 

Grainger, M., Piras, S., Righi, S., Setti, M., Stewart, G., Vittuari, M.. 2018. Behavioural economics: Linking Bayesian and agent-based models to assess consumer food waste. REFRESH Deliverable D4.4

Deliverable: 

D4.4

ISBN: 

978-94-6343-994-7

Work package: